A Brave New World: The Rise of Agentic AI in Rapid Cycle RWE Analytics

Traditional no-code tools like drag-and-drop cohort builders eliminated the need for manual programming, which was a significant advancement. However, they remain fundamentally reactive; users still need to specify which variables to select, understand their data distributions, and manually translate concepts into diagnosis codes. Agentic AI systems go further by autonomously interpreting natural language questions, reasoning about the analytical approach needed, and executing entire analyses from start to finish. They proactively identify relevant cohorts and roadblocks and iterate until producing a satisfactory answer.

Traditional end-to-end RWE analyses often take months as analysts wrangle data, build models, validate results, and report insights. Early no-code platforms reduced this significantly. But agentic AI platforms promise order-of-magnitude improvements beyond even these gains, potentially reducing weeks of manual work to hours through autonomous task orchestration and natural language interfaces.

Yes, this is one of the major advantages of agentic AI for RWE. Only about 20% of EHR data is structured, leaving the vast majority of potential insights untapped using traditional methods. Agentic platforms leverage natural language processing and generative AI to extract information from clinical notes, imaging reports, and other unstructured sources, transforming them into structured formats for analysis. This capability removes a significant bottleneck that has historically made working with unstructured data labor-intensive and time-consuming.

Agentic AI outputs should be treated as drafts to be verified and refined rather than definitive results. The FDA's January 2025 draft guidance on AI for regulatory decision-making emphasizes the need to thoroughly document data sources, algorithms, assumptions, and validation procedures. Teams should implement human-in-the-loop approaches where experts validate and correct AI outputs, use techniques like retrieval-augmented generation to mitigate hallucinations, and consider medical-grade LLMs trained on robust medical terminology. Continuous methodological oversight and bias identification remain critical competencies for RWE teams adopting these tools.

As agentic AI transforms RWE workflows, teams will need to develop new competencies in three key areas: prompt engineering (crafting effective natural language queries to guide AI systems), AI oversight (understanding how to validate and correct AI-generated outputs), and bias mitigation (identifying and addressing potential biases in AI-driven analyses). While these systems automate many technical tasks, human expertise remains essential for study design, methodological decisions, interpretation of results, and ensuring scientific accuracy throughout the evidence generation process.